Staff Data Scientist

Toast Toast · Enterprise · United States · Remote · R & D : Engineering : Commerce

Staff Data Scientist at Toast, leading the design and development of scalable ML systems for restaurant operations (recommendation, forecasting, targeting, personalization). Owns full ML lifecycle, designs advanced models, collaborates with stakeholders, guides architecture, mentors junior scientists, and identifies new business value opportunities using AI. Requires 7+ years of experience, strong ML/stats knowledge, production ML delivery, distributed systems, cloud platforms (AWS), Python/SQL, and LLM service experience. Bonus for advanced degrees, MLOps, fine-tuning LLMs, and RLHF.

What you'd actually do

  1. Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.
  2. Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.
  3. Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.
  4. Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.
  5. Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.

Skills

Required

  • 7+ years of experience in data science
  • Deep knowledge of statistical modeling, machine learning
  • model evaluation
  • Experience working with real-world product data at scale
  • Experience with distributed data processing and training
  • real-time inference
  • ML Ops frameworks
  • Prior experience mentoring other data scientists or acting as a tech lead
  • Experience leading experimentation (e.g., A/B testing)
  • causal inference
  • real-time decision systems
  • Proficiency in Python and SQL
  • experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow)
  • Strong grasp of software engineering principles
  • Hands-on experience with cloud platforms (preferably AWS)
  • Excellent communication skills
  • Strong business acumen

Nice to have

  • An advanced degree in Computer Science, Statistics, or a related STEM field
  • Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability
  • Experience fine-tuning LLMs
  • applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment

What the JD emphasized

  • proven track record of delivering production ML systems that drive measurable impact
  • translating ambiguous problems into well-scoped ML solutions
  • building services on top of LLMs in a large scale production environment

Other signals

  • delivering production ML systems that drive measurable impact
  • translating ambiguous problems into well-scoped ML solutions
  • building services on top of LLMs in a large scale production environment